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GPT-4.1 Nano vs Qwen3.5 397B A17B

Qwen3.5 397B A17B leads GPT-4.1 Nano on the Artificial Analysis Intelligence Index, 21.4 to 7.8, and GPT-4.1 Nano costs 5.8x less per output token ($0.40 vs $2.34 per million). Updated October 22, 2026; 44 benchmarks have a result for both.

Replace GPT-4.1 Nano Replace Qwen3.5 397B A17B
GPT-4.1 NanoOpenAI
Coding
No data
Overall
24.7
Output $/M
$0.40
0
Qwen3.5 397B A17BQwen
Coding
No data
Overall
57.1
Output $/M
$2.34
44
vs boards won of 44 shared

GPT-4.1 Nano or Qwen3.5 397B A17B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GPT-4.1 Nano for output tokens 5.8x cheaper ($0.40 vs $2.34 per million) and a larger context window (1.05M against 262K tokens).

Model AGPT-4.1 NanoOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5 397B A17BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
44
GPT-4.1 Nano only
27
Qwen3.5 397B A17B only
22
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-4.1 Nano not benchmarked, Qwen3.5 397B A17B not benchmarked.
Widest category
Agentic work, 78.4 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GPT-4.1 Nano 17.3% against Qwen3.5 397B A17B 95.6%, 78.4 points apart on a 0 to 100 scale.
Output price
GPT-4.1 Nano $0.40 against Qwen3.5 397B A17B $2.34 per million output tokens, 5.8x apart.
Context window
GPT-4.1 Nano takes 1.05M tokens, Qwen3.5 397B A17B 262K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-4.1 NanoBarsQwen3.5 397B A17BTies
Coding03
Intelligence02
Agentic work01
Reasoning06
Math03
Knowledge04
Web design06
General019
All shared boards044
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4.1 Nano and Qwen3.5 397B A17B
BenchmarkGPT-4.1 NanoQwen3.5 397B A17BLeader
Aider polyglot8.9%Not measuredOnly GPT-4.1 Nano measured
LMArena WebDevNot measured1400Only Qwen3.5 397B A17B measured
AA Coding Index11.1Not measuredOnly GPT-4.1 Nano measured
LiveCodeBench (Vals AI)42.7%Not measuredOnly GPT-4.1 Nano measured
LiveCodeBench (Artificial Analysis run)32.6%Not measuredOnly GPT-4.1 Nano measured
SciCodeNot measured44.8%Only Qwen3.5 397B A17B measured
Terminal-Bench Hard (Artificial Analysis run)3.8%40.9%Qwen3.5 397B A17B
WeirdML19.0%Not measuredOnly GPT-4.1 Nano measured
ALE-BenchNot measured621.9Only Qwen3.5 397B A17B measured
LMArena Text: coding13061464Qwen3.5 397B A17B
LMArena Text: software and IT13051463Qwen3.5 397B A17B
LMArena WebDev: ReactNot measured1388Only Qwen3.5 397B A17B measured
LMArena WebDev: HTMLNot measured1410Only Qwen3.5 397B A17B measured
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GPT-4.1 Nano and Qwen3.5 397B A17B
BenchmarkGPT-4.1 NanoQwen3.5 397B A17BLeader
AA Intelligence Index7.821.4Qwen3.5 397B A17B
Epoch Capabilities Index129.5146.8Qwen3.5 397B A17B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-4.1 Nano and Qwen3.5 397B A17B
BenchmarkGPT-4.1 NanoQwen3.5 397B A17BLeader
τ²-Bench Telecom17.3%95.6%Qwen3.5 397B A17B
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
Berkeley Function Calling Leaderboard (BFCL)33.1%Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GPT-4.1 Nano and Qwen3.5 397B A17B
BenchmarkGPT-4.1 NanoQwen3.5 397B A17BLeader
GPQA Diamond50.8%Not measuredOnly GPT-4.1 Nano measured
MMMU Pro (Vals AI)55.0%Not measuredOnly GPT-4.1 Nano measured
GPQA Diamond (Artificial Analysis run)51.2%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)3.8%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)48.9%86.4%Qwen3.5 397B A17B
ARC-AGI0.0%Not measuredOnly GPT-4.1 Nano measured
ARC-AGI-20.0%Not measuredOnly GPT-4.1 Nano measured
Chess puzzles0.0%Not measuredOnly GPT-4.1 Nano measured
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
LMArena Text: hard prompts12861448Qwen3.5 397B A17B
LMArena Text: expert12711462Qwen3.5 397B A17B
LMArena Text: hard prompts in English12931450Qwen3.5 397B A17B
LMArena Vision: diagramsNot measured1277Only Qwen3.5 397B A17B measured
LMArena Vision: homeworkNot measured1292Only Qwen3.5 397B A17B measured
Kagi LLM Benchmark33.3%Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GPT-4.1 Nano and Qwen3.5 397B A17B
BenchmarkGPT-4.1 NanoQwen3.5 397B A17BLeader
AIME26.5%Not measuredOnly GPT-4.1 Nano measured
MATH 500 (Vals AI)80.2%Not measuredOnly GPT-4.1 Nano measured
MGSM (Vals AI)69.3%Not measuredOnly GPT-4.1 Nano measured
AA Math Index24.0Not measuredOnly GPT-4.1 Nano measured
AIME 2025 (Artificial Analysis run)24.0%Not measuredOnly GPT-4.1 Nano measured
OTIS Mock AIME28.9%88.9%Qwen3.5 397B A17B
FrontierMath, tiers 1 to 3Not measured31.2%Only Qwen3.5 397B A17B measured
MATH level 570.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: math12741453Qwen3.5 397B A17B
LMArena Text: mathematical work12741454Qwen3.5 397B A17B
MathArenaNot measured19.2%Only Qwen3.5 397B A17B measured
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4.1 Nano and Qwen3.5 397B A17B
BenchmarkGPT-4.1 NanoQwen3.5 397B A17BLeader
MMLU Pro63.5%Not measuredOnly GPT-4.1 Nano measured
LegalBench (Vals AI)61.1%Not measuredOnly GPT-4.1 Nano measured
CorpFin (Vals AI)42.1%Not measuredOnly GPT-4.1 Nano measured
TaxEval (Vals AI)60.8%Not measuredOnly GPT-4.1 Nano measured
MortgageTax (Vals AI)52.8%Not measuredOnly GPT-4.1 Nano measured
MedQA (Vals AI)68.2%Not measuredOnly GPT-4.1 Nano measured
MMLU Pro (Artificial Analysis run)65.7%Not measuredOnly GPT-4.1 Nano measured
SimpleQA Verified6.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: science12971457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare12761455Qwen3.5 397B A17B
LMArena Text: legal and government13001441Qwen3.5 397B A17B
LMArena Text: business and finance12671435Qwen3.5 397B A17B
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for GPT-4.1 Nano and Qwen3.5 397B A17B
BenchmarkGPT-4.1 NanoQwen3.5 397B A17BLeader
Design Arena: websites9731190Qwen3.5 397B A17B
Design Arena: UI components9131161Qwen3.5 397B A17B
Design Arena: game dev9701140Qwen3.5 397B A17B
Design Arena: data viz8911175Qwen3.5 397B A17B
Design Arena: 3D9241160Qwen3.5 397B A17B
Design Arena: all code categories9621179Qwen3.5 397B A17B
Design Arena: SVGNot measured1139Only Qwen3.5 397B A17B measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for GPT-4.1 Nano and Qwen3.5 397B A17B
BenchmarkGPT-4.1 NanoQwen3.5 397B A17BLeader
IFBench32.0%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)20.3%77.3%Qwen3.5 397B A17B
DTBench52.5%87.5%Qwen3.5 397B A17B
LMCA5.5%37.9%Qwen3.5 397B A17B
Fiction.LiveBench25.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text12851437Qwen3.5 397B A17B
LMArena Text: instruction following12661424Qwen3.5 397B A17B
LMArena Text: creative writing12601402Qwen3.5 397B A17B
LMArena Vision10631262Qwen3.5 397B A17B
LMArena Text: English13031443Qwen3.5 397B A17B
LMArena Text: non-English12601430Qwen3.5 397B A17B
LMArena Text: Chinese12701498Qwen3.5 397B A17B
LMArena Text: German12881445Qwen3.5 397B A17B
LMArena Text: Japanese11981429Qwen3.5 397B A17B
LMArena Text: KoreanNot measured1384Only Qwen3.5 397B A17B measured
LMArena Text: FrenchNot measured1462Only Qwen3.5 397B A17B measured
LMArena Text: SpanishNot measured1441Only Qwen3.5 397B A17B measured
LMArena Text: Russian12611430Qwen3.5 397B A17B
LMArena Text: PolishNot measured1447Only Qwen3.5 397B A17B measured
LMArena Text: multi-turn12771446Qwen3.5 397B A17B
LMArena Text: longer queries12831441Qwen3.5 397B A17B
LMArena Text: writing and language12611415Qwen3.5 397B A17B
LMArena Text: entertainment and media12431399Qwen3.5 397B A17B
LMArena Vision: English11011268Qwen3.5 397B A17B
LMArena Vision: ChineseNot measured1314Only Qwen3.5 397B A17B measured
LMArena Vision: reading textNot measured1271Only Qwen3.5 397B A17B measured
LMArena Vision: humorNot measured1262Only Qwen3.5 397B A17B measured
LMArena Vision: captioningNot measured1230Only Qwen3.5 397B A17B measured
LMArena Vision: recognizing thingsNot measured1265Only Qwen3.5 397B A17B measured
LMArena Vision: creative writingNot measured1260Only Qwen3.5 397B A17B measured
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4.1 Nano and Qwen3.5 397B A17B
BenchmarkGPT-4.1 NanoQwen3.5 397B A17BLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-4.1 Nano$0.0003Fits in one request
Qwen3.5 397B A17B$0.0016Fits in one request

GPT-4.1 Nano costs less

Repository review

50K input + 3K output tokens

GPT-4.1 Nano$0.0062Fits in one request
Qwen3.5 397B A17B$0.027Fits in one request

GPT-4.1 Nano costs less

Agent loop

200K input + 10K output tokens

GPT-4.1 Nano$0.024Fits in one request
Qwen3.5 397B A17B$0.101Fits in one request

GPT-4.1 Nano costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-4.1 Nano and Qwen3.5 397B A17B
SpecificationOpenAI GPT-4.1 NanoQwen3.5 397B A17B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 14, 2025February 16, 2026
Context window1.05M262K
Input $/Mtok$0.10$0.39
Output $/Mtok$0.40$2.34
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 Nano better than Qwen3.5 397B A17B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for output tokens 5.8x cheaper ($0.40 vs $2.34 per million) and a larger context window (1.05M against 262K tokens).

Which is cheaper, GPT-4.1 Nano or Qwen3.5 397B A17B?

GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4.1 Nano or Qwen3.5 397B A17B?

GPT-4.1 Nano, with 1.05M tokens against 262K.

How many benchmarks is the GPT-4.1 Nano vs Qwen3.5 397B A17B comparison based on?

44 benchmarks have a result for both models as of October 22, 2026. GPT-4.1 Nano's coding index rests on 0 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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